53 citations · 142 across the 15 of their papers we have counts for
16 papers
From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations
Sven Weinzierl, Sandra Zilker, Annina Liessmann +3
Event logs reflect the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms these data into value by…
FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring
Felix Möhrlein, Martin Käppel, Julian Neuberger +4
Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without cons…
A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring
Martin Käppel, Julian Neuberger, Felix Möhrlein +3
Predictive process monitoring enables organizations to proactively react and intervene in running instances of a business process. Given an incomplete process instance, predictions…
CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations
Julian Rosenberger, Lukas Wolfrum, Sven Weinzierl +2
The rapidly evolving labor market, driven by technological advancements and economic shifts, presents significant challenges for traditional job matching and consultation services.…
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Sven Kruschel, Nico Hambauer, Sven Weinzierl +3
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance ad…
Recent Advances in Data-Driven Business Process Management
Lars Ackermann, Martin Käppel, Laura Marcus +21
The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm sh…
Machine learning in business process management: A systematic literature review
Sven Weinzierl, Sandra Zilker, Sebastian Dunzer +1
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are u…
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Sandra Zilker, Sven Weinzierl, Mathias Kraus +2
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage…
Guiding Text-to-Text Privatization by Syntax
Stefan Arnold, Dilara Yesilbas, Sven Weinzierl
Metric Differential Privacy is a generalization of differential privacy tailored to address the unique challenges of text-to-text privatization. By adding noise to the representati…
Driving Context into Text-to-Text Privatization
Stefan Arnold, Dilara Yesilbas, Sven Weinzierl
\textit{Metric Differential Privacy} enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy…
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
Patrick Zschech, Sven Weinzierl, Nico Hambauer +2
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the intern…
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
An Nguyen, Srijeet Chatterjee, Sven Weinzierl +3
Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next…
Prescriptive Business Process Monitoring for Recommending Next Best Actions
Sven Weinzierl, Sebastian Dunzer, Sandra Zilker +1
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning t…
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
Sven Weinzierl, Sandra Zilker, Jens Brunk +3
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve pro…
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
Matthias Stierle, Sven Weinzierl, Maximilian Harl +1
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models…
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
S. Weinzierl, S. Zilker, J. Brunk +5
Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially,…